AI and Bioinformatics: Genomic Data Analysis
Apply AI to genomic data with hands-on WEKA practice
This course introduces practical AI approaches for genomic data analysis, combining machine learning and deep learning ideas with hands-on tool use to help researchers interpret sequencing, protein function, and gene expression datasets.
Through case studies and guided exercises using WEKA, learners practice data preprocessing, model selection, and visualization techniques that support bioinformatics research. By the end of the course participants should be able to apply WEKA workflows, explain core ML and neural network concepts (including CNN and RNN ideas), and produce research-ready visualisations and manuscripts for genomics projects.
At a Glance
AI and Bioinformatics: Genomic Data Analysis is a short online course offered by Taipei Medical University that focuses on practical AI techniques for analysing genomic and other bioinformatics data.
It covers machine learning and deep learning concepts, hands-on use of WEKA, and research-focused skills such as data visualisation and writing bioinformatics papers.
| Level | Beginner to Intermediate |
| Rating | 4.6 out of 5 |
| Duration | 3+ weeks |
| Languages | English |
| Certificate | Certificate available on completion (requires completing 90% of steps and all assessments) |
| Access | Free to join with optional paid upgrade for extended access and certificate |
| Course includes |
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| Price | Free to join; optional paid upgrade for certificate and extended access |
What This Course Teaches
The course frames outcomes as measurable competencies that learners can demonstrate in bioinformatics research workflows.
Learners will be able to explain core AI and ML concepts, apply WEKA to analyse genomic data, produce clear data visualisations, and write research-ready bioinformatics papers.
How the Course Is Structured
The course is organised into 8 modules across three weeks, with weekly themes that move from feature learning through deep learning to research writing and assessment.
It combines short instructional videos, readings, practical WEKA exercises, and social learning activities to be completed alongside an end-of-course assessment.
Curriculum overview
01Review of AI in Bioinformatics▾
A brief refresher of core AI concepts introduced in the preceding Artificial Intelligence in Bioinformatics short course to re-establish foundational knowledge.
02Feature Learning▾
Introduces feature learning techniques that enable algorithms to identify and extract informative patterns from raw bioinformatics data.
03Deep learning algorithm▾
Explains how artificial neural networks use multiple layers to learn representations and detect complex patterns in biological datasets.
04Deep learning and bioinformatics▾
Covers applications, opportunities, and challenges of applying deep learning approaches across genomics, protein prediction, and gene expression analysis.
05Weka and Deep Learning▾
Focuses on integrating deep learning workflows with the WEKA platform, demonstrating implementations and discussing compatible frameworks.
06Research Flowchart and Dabta Visualization▾
Presents a step-by-step research flowchart and practical techniques for data visualisation to support analysis and reporting in bioinformatics.
07Bioinformatics Paper Example▾
Walks through a published paper example that demonstrates applying deep neural networks with biological subwords to detect protein S-sulfenylation sites.
08Assessment Time▾
Concludes the course with an assessment and reflective activities to consolidate learning and plan further use of WEKA.
- Assessment quiz
- Discussion on course feedback
- Planning next steps for using WEKA
Audience & Requirements
This course is aimed at learners who want to apply AI methods to biological data, especially those working in or studying bioinformatics and related research fields.
Intended participants include biology students, laboratory researchers, and graduate-level learners preparing to use machine learning in genomics and protein analysis.
- Biology or bioinformatics students seeking applied AI skills.
- Researchers and lab scientists who analyse genomic or proteomic data.
- Graduate students preparing to write or contribute to bioinformatics papers.
- Professionals who want to integrate WEKA-based workflows into research projects.
- Familiarity with basic biology and bioinformatics concepts.
- Some prior exposure to AI/ML is recommended — the preceding short course on Artificial Intelligence in Bioinformatics is suggested.
- Ability to install and run WEKA (Java runtime environment) and work with standard bioinformatics datasets.
Final Verdict
Given its favourable user rating and focused, practice-oriented syllabus, this course is a cost-effective way to gain applied AI skills for bioinformatics research.
Its free-to-join model with an optional paid upgrade and an available completion certificate makes it a low-risk option for students and researchers seeking hands-on WEKA experience.
The course is recommended for biology students, lab researchers, and graduate learners with some domain familiarity or who are willing to take the recommended precursor material; it is less well suited as a first introduction for learners with no prior biology or AI background.
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